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- W3044427643 abstract "This work investigates semi-supervised training of acoustic models (AM) with the lattice-free maximum mutual information (LF-MMI) objective in practically relevant scenarios with a limited amount of labeled in-domain data. An error detection driven semi-supervised AM training approach is proposed, in which an error detector controls the hypothesized transcriptions or lattices used as LF-MMI training targets on additional unlabeled data. Under this approach, our first method uses a single error-tagged hypothesis whereas our second method uses a modified supervision lattice. These methods are evaluated and compared with existing semi-supervised AM training methods in three different matched or mismatched, limited data setups. Word error recovery rates of 28 to 89% are reported." @default.
- W3044427643 created "2020-07-29" @default.
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- W3044427643 date "2020-10-25" @default.
- W3044427643 modified "2023-10-18" @default.
- W3044427643 title "On Semi-Supervised LF-MMI Training of Acoustic Models with Limited Data" @default.
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- W3044427643 doi "https://doi.org/10.21437/interspeech.2020-2242" @default.
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